DISCLAIMER:
Entry of taxonomy/keywords during proffered abstract submission was optional.
Not all abstracts will appear in search results.
PO-GePV-M-186 | Quantitative Evaluation and Feasibility Study of Using AI Denoising Technique to Minimize Image Dose of 2.5MV Beam H Kuo1*, S Lim1, S Lin2, J Sillanpaa1, L Cervino1, (1) Memorial Sloan Kettering Cancer Center, New York, NY (2) Norwalk Hospital, Norwalk, CT |
SU-E-207-1 | Noise2noise Deep Learning Based Acceleration for MRI Echo-Planar Imaging L Qin1*, C Lindsay2, A Konik1, G Young3, (1) Dana-Farber Cancer Institute, Boston, MA, (2) University Of Massachusetts Chan Medical School, Worcester, MA (3) Brigham And Women's Hospital, Boston, MA |
TH-D-207-6 | Texture Transformer Super-Resolution (TTSR) for Patient CT Images S Zhou1*, L Yu2, M Jin1, (1) University of Texas at Arlington, Arlington, TX, (2) Mayo Clinic, Rochester, MN |
WE-A-201-4 | Joint K-B Space Image Reconstruction and Data Fitting for Diffusion-Weighted Magnetic Resonance Imaging J Deng*, X Jia, The University of Texas Southwestern Medical Ctr, Garland, TX |